Your agent follows your conventions. Until it doesn’t.
The context fills up and the rules get compacted away. A subagent starts fresh and never saw them. Two skills say different things. Then the branch is feat/add-export on Monday and add-export-feature on Tuesday, a commit has no footer, a .env file gets staged.
Most AI coding workflows stop where the fun ends: the agent writes code, and you do the rest. Create the branch, write the commit, open the PR, refresh the checks page, read the review comments, fix them, push, refresh again.
AI agents are getting scary good. Tools like clawbot can understand your codebase, write features, fix bugs, and commit changes - all without much human input.
But that doesn’t mean you should let them run wild.
Running AI agents locally is great until you realize they have full access to your machine. One wrong command and you could lose important files.
Docker sandboxes solve this, but the official one lacks flexibility - no persistent workspaces, no multi-instance support, limited customization.
So here’s the situation: our frontend team had this growing backlog of UI tweaks.
Buttons slightly off, wrong font sizes, missing translations, components that didn’t quite match what the designers put in Figma.
